Static, dynamic and electronic properties of some trivalent liquid rare earth metals near melting: <i>ab initio</i> and neural network simulations

We report a study on several static and dynamic properties of the early trivalent liquid rare-earth metals at thermodynamic conditions near their respective melting points. It has been performed by resorting to machine learning (ML) techniques, in which the associated neural network-based interatomic potentials were derived from <i>ab initio</i> molecular dynamics simulations within Hubbard-corrected density functional theory. We report the results obtained for the static structural properties, including an analysis of the local short-range order. Single-particle and collective dynamic properties have also been obtained, from which transport coefficients and wavevector-dependent dispersion relations are evaluated. The results show a quite homogeneous behavior of the structural, dynamic, and transport properties throughout the series. The electronic properties have been obtained from the <i>ab initio</i> simulations, and show important discrepancies with respect to the low temperature solids, portraying a more band-like picture of the 4f states in the liquid.